With the increasing interest in artificial intelligence, machine learning (ML) approaches are introduced for heuristic abnormality detection. To this end, several ML techniques using shallow linear regression models like support vector machine (SVM) [1]} and random forests [2]} have been applied for crack detection. Such learning approaches provide an adaptive solution in disposing of a variety of crack patterns. However, the prediction accuracy may be limited by the model simplification and the available computational capacity in practice. On the other hand, deep convolutional neural networks (DCNN) [3]} have been proposed as a probabilistic learning framework with a modest processing time that becomes very attractive for real-time applications. This technique provides an effective solution to challenges in semantic segmentation [4]} owing to the capability of multiple-level abstraction and deep breakdown for identified features. Such promising results suggest the use of DCNN to identify a surface defect with binary segmentation.
